Parallel matrix factorization for low-rank tensor completion

Overview

This paper introduces a new method that recovers missing entries of low-rank tensors.
This problem is known as the low-rank tensor completion (LRTC) problem.

An approach for LRTC is to unfold the tensor as matrices and then apply nuclear-norm
minimization to complete these matrices (and thus the tensor). It works well but
is quite slow and cannot solve large-scale problems.
To tackle this issue, instead of minimizing nuclear-norms,
we recover the low-rank factorizations of those unfolding matrices. We call the
approach “Tensor completion by parallel matrix factorization” (TMac).
We found it faster and having much better recovery rate. An open question is
its theoretical guarantee.

Our method can be generalized to the general recovery of low-rank tensors.

Our formulation and method

We aim at recovering a low-rank tensor from partial observations , where is the index set of observed entries, and keeps the entries in and zeros out others. We apply low-rank matrix factorization to each mode unfolding of by finding matrices such that for , where is the estimated rank, either fixed or adaptively updated. Introducing one common variable to relate these matrix factorizations, we solve the following model

where and . In the model, , , are weights and satisfy .
We use alternating least squares method to solve the model.

Numerical results

Our model is non-convex jointly with respect to and . Although a global solution is not guaranteed, we demonstrate by numerical experiments that our algorithm can reliably recover a wide variety of low-rank tensors, such as the following phase transition plots. In the picture, each target tensor , where and have Gaussian random entries. (a) FaLRTC: the tensor completion method in .
(b) MatComp: first reshape the tensor as a matrix and then use the matrix completion solver LMaFit in . (c) TMac-fix: our method with and fixed to . (d) TMac-inc: our method with and using rank-increasing strategy starting from . (e) TMac-dec: our method with and using rank-decreasing strategy starting from .

The results show that our method performs much better than the other two methods.